There is a vast amount of electronic documents that are created and stored on a daily basis. In order for the documents to be searchable, content from the documents is often extracted, catalogued, and organized in a centralized database. In some implementations, documents may be organized into clusters of documents, where each cluster includes documents having the same or similar attribute(s), such as an overall topic. Document organization is helpful for many purposes. For example, the discovery phase of a lawsuit may involve the review of millions of documents, where the amount of time needed to review the documents is significantly reduced when the documents are organized according to some scheme.
However, even when the documents are organized (e.g., into clusters), it may prove difficult to ascertain which documents may be relevant to a particular query and/or which groups of documents may be similar to other groups of documents. In particular, a user may wish to identify only a few clusters, out of many, that may be relevant to a query. However, current technologies are limited in their abilities to effectively and accurately generate visualizations that depict similarities among documents, and in particular, depict similarities among multiple clusters of documents.
Accordingly, there is an opportunity for systems and methods to analyze electronic documents and generate visualizations of similarities among clusters of the documents.